Bibliographic record
Abstract
A wind turbine is a modern machine that generates electricity from wind.Wind turbines generate four types of noise: tonal, broadband, low frequency, and impulsive.Another way to look at wind turbine noise is to consider its sources.There are two fundamental categories, mechanical and aerodynamic.Mechanical noise is transmitted along the structure o f the turbine and is radiated from its surfaces.Aerodynamic noise is produced by the flow o f air over the blades.In the United States, wind farm siting often requires compliance with state and/or local noise regulations.Common practice is to determine minimum setback distances from residences to comply with the most stringent noise limit.Geographic Information Systems (GIS) is a valuable tool in this type o f analysis, particularly when current aerial photographs are available in GIS-ready format.Although recent technology advances has decreased overall noise levels, tonal noise still remains a concern during the planning process.Detailed meteorological data is available for most portions o f the United States, however it is not commonly used to evaluate wind turbine noise.The authors o f this paper are studying the creation o f a GIS-based model that utilizes detailed met data in the propagation o f wind turbine noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.195 | 0.130 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".